Abstract

In traditional research, data fusion is referred to as multi-sensor data fusion. The theory is that data from multiple sources can be combined to provide more accurate, reliable and meaningful information than that provided by a single data source. Applications in this field of study were originally in the military domain; more recently, investigations for its application in various civilian domains (eg: computer security) have been undertaken. Multi-sensor data fusion as applied to biometric authentication is termed multi-modal biometrics. The objective of this study was to apply feature level fusion of fingerprint feature and keystroke dynamics data for authentication purposes, utilizing Artificial Neural Networks (ANNs) as a classifier. Data fusion was performed adopting the cooperative paradigm, a less researched approach. This approach necessitates feature subset selection to utilize the most discriminatory data from each source. Experimental results returned a false acceptance rate (FAR) of 0.0 and a worst case false rejection rate (FRR) of 0.0006, which were comparable to—and in some cases, slightly better than—other research using the cooperative paradigm.